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D.J. Eisenstein (University of Chicago, IAS), M. Zaldarriaga (Institute for Advanced Study)
We reconsider the inference of spatial power spectra from angular clustering data and show how to include correlations in both the angular correlation function and the spatial power spectrum. We present a new inversion technique based on singular value decomposition that allows one to propagate the covariance matrix on the angular correlation function through to that of the spatial power spectrum and to reconstruct smooth power spectra without underestimating the errors. We apply our methods to the angular correlations of the APM galaxy survey and show that inclusion of the full covariance matrices loosens the constraints on large-scale structure by over a factor of two relative to previous work.
D.J.E. and M.Z. are supported by NASA/STScI Hubble Fellowships.